From 6fa4331771179487dc89330dd11214dbf7a78155 Mon Sep 17 00:00:00 2001 From: Nirant Kasliwal Date: Mon, 15 Jan 2024 12:02:22 +0530 Subject: [PATCH 01/16] Add saved models to .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index b59d488e..5e34b4d6 100644 --- a/.gitignore +++ b/.gitignore @@ -176,3 +176,4 @@ fooling_around/fast-multilingual-e5-large/sentencepiece.bpe.model fooling_around/fast-multilingual-e5-large/special_tokens_map.json fooling_around/fast-multilingual-e5-large/tokenizer_config.json fooling_around/fast-multilingual-e5-large/tokenizer.json +docs/examples/saved_models/* \ No newline at end of file From f3bf86ee4e86ef7be3721d226aa592868fae521c Mon Sep 17 00:00:00 2001 From: Nirant Kasliwal Date: Mon, 15 Jan 2024 12:02:40 +0530 Subject: [PATCH 02/16] ONNX Port --- docs/examples/To_ONNX.ipynb | 284 ++++++++++++++++++++++++ fooling_around/01_ONNX_Port.ipynb | 357 ------------------------------ 2 files changed, 284 insertions(+), 357 deletions(-) create mode 100644 docs/examples/To_ONNX.ipynb delete mode 100644 fooling_around/01_ONNX_Port.ipynb diff --git a/docs/examples/To_ONNX.ipynb b/docs/examples/To_ONNX.ipynb new file mode 100644 index 00000000..5f4dc680 --- /dev/null +++ b/docs/examples/To_ONNX.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7412714c", + "metadata": {}, + "source": [ + "# Porting to ONNX\n", + "\n", + "This notebook demonstrates how to port models from Transformers/PyTorch package to ONNX. It is based on the [Optimum](https://github.com/huggingface/optimum) library.\n", + "\n", + "## Installation\n", + "\n", + "We use [poetry](https://python-poetry.org/docs/cli) to manage dependencies. To install the dependencies, run:\n", + "\n", + "```bash\n", + "poetry install\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0e9dbcde", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c37e1fda-c7f1-46e7-a5d4-19fa05c36ac1", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path\n", + "from typing import List, Tuple, Any\n", + "\n", + "import numpy as np\n", + "import time\n", + "from torch import Tensor\n", + "from transformers import AutoTokenizer, AutoModel\n", + "from dotenv import load_dotenv\n", + "\n", + "load_dotenv() # take environment variables from .env.\n", + "from optimum.onnxruntime import AutoOptimizationConfig, ORTModelForFeatureExtraction, ORTOptimizer\n", + "from optimum.pipelines import pipeline\n", + "import torch.nn.functional as F" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b1ecf0b6-db81-4da3-b47f-e31460ccfbf1", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the tokenizer and PyTorch model from HuggingFace Transformers\n", + "model_id = \"BAAI/bge-small-en-v1.5\"\n", + "hf_model = AutoModel.from_pretrained(model_id)\n", + "hf_tokenizer = AutoTokenizer.from_pretrained(model_id)\n", + "quantize = True" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a38f5aed", + "metadata": {}, + "outputs": [], + "source": [ + "# The input texts can be in any language, not just English.\n", + "# Each input text should start with \"query: \" or \"passage: \", even for non-English texts.\n", + "# For tasks other than retrieval, you can simply use the \"query: \" prefix.\n", + "multilingual_queries = [\n", + " \"query: how much protein should a female eat\",\n", + " \"query: 南瓜的家常做法\",\n", + " \"query: भारत का राष्ट्रीय खेल कौन-सा है?\", # Hindi text\n", + " \"query: భారత్ దేశంలో రాష్ట్రపతి ఎవరు?\", # Telugu text\n", + " \"query: இந்தியாவின் தேசிய கோப்பை எது?\", # Tamil text\n", + " \"query: ಭಾರತದಲ್ಲಿ ರಾಷ್ಟ್ರಪತಿ ಯಾರು?\", # Kannada text\n", + " \"query: ഇന്ത്യയുടെ രാഷ്ട്രീയ ഗാനം എന്താണ്?\", # Malayalam text\n", + "]\n", + "\n", + "english_texts = [\n", + " \"India: Where the Taj Mahal meets spicy curry.\",\n", + " \"Machine Learning: Turning data into knowledge, one algorithm at a time.\",\n", + " \"Python: The language that makes programming a piece of cake.\",\n", + " \"fastembed: Accelerating embeddings for lightning-fast similarity search.\",\n", + " \"Qdrant: The ultimate tool for high-dimensional indexing and search.\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9f8c761c", + "metadata": {}, + "outputs": [], + "source": [ + "def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:\n", + " last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)\n", + " return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]\n", + "\n", + "\n", + "def hf_embed(model_id: str, inputs: List[str]):\n", + " # Tokenize the input texts\n", + " batch_dict = hf_tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors=\"pt\")\n", + "\n", + " outputs = hf_model(**batch_dict)\n", + " embeddings = average_pool(outputs.last_hidden_state, batch_dict[\"attention_mask\"])\n", + "\n", + " # normalize embeddings\n", + " embeddings = F.normalize(embeddings, p=2, dim=1)\n", + " return embeddings.detach().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "69bb4501", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.05485763, 0.08136623, -0.00395789, ..., 0.02512371,\n", + " -0.03349504, -0.0593129 ],\n", + " [ 0.01078518, 0.01582215, 0.04614557, ..., -0.01674951,\n", + " -0.00244641, -0.06179965],\n", + " [-0.06607923, -0.01235531, -0.00689854, ..., 0.10634594,\n", + " 0.12025263, -0.05135345],\n", + " [-0.07568254, 0.00908228, -0.02221818, ..., 0.00177038,\n", + " -0.0325426 , 0.05233581],\n", + " [-0.07008213, 0.02070545, 0.02720274, ..., -0.01158645,\n", + " -0.01457597, 0.01262206]], dtype=float32)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hf_embed(inputs=english_texts, model_id=model_id)" + ] + }, + { + "cell_type": "markdown", + "id": "bc9d1594", + "metadata": {}, + "source": [ + "## Load the model using ORTModelForFeatureExtraction" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "451dbd16", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Framework not specified. Using pt to export to ONNX.\n", + "Using the export variant default. Available variants are:\n", + " - default: The default ONNX variant.\n", + "Using framework PyTorch: 2.1.2\n", + "Overriding 1 configuration item(s)\n", + "\t- use_cache -> False\n", + "/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.9/site-packages/optimum/onnxruntime/configuration.py:770: FutureWarning: disable_embed_layer_norm will be deprecated soon, use disable_embed_layer_norm_fusion instead, disable_embed_layer_norm_fusion is set to True.\n", + " warnings.warn(\n", + "The argument use_external_data_format in the ORTOptimizer.optimize() method is deprecated and will be removed in optimum 2.0.\n", + "Optimizing model...\n", + "There is no gpu for onnxruntime to do optimization.\n", + "Configuration saved in saved_models/ort_config.json\n", + "Optimized model saved at: saved_models (external data format: False; saved all tensor to one file: True)\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('saved_models')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = ORTModelForFeatureExtraction.from_pretrained(model_id, export=True)\n", + "\n", + "# Load the optimization configuration detailing the optimization we wish to apply\n", + "if quantize:\n", + " optimization_config = AutoOptimizationConfig.O4()\n", + " optimization_config = AutoOptimizationConfig.O4()\n", + " optimizer = ORTOptimizer.from_pretrained(model)\n", + "\n", + "save_dir = Path(\"saved_models\") # Path to save the optimized model, working directory\n", + "optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config, use_external_data_format=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2ea5bd5a", + "metadata": {}, + "outputs": [], + "source": [ + "model = ORTModelForFeatureExtraction.from_pretrained(save_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "68d7ab8b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "402e707a28ea4b2baf6c7eea382933b7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model_optimized.onnx: 0%| | 0.00/66.5M [00:00 Tensor:\n", - " last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)\n", - " return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]\n", - "\n", - "\n", - "def hf_embed(model_id: str, inputs: List[str]):\n", - " # Tokenize the input texts\n", - " batch_dict = hf_tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors=\"pt\")\n", - "\n", - " outputs = hf_model(**batch_dict)\n", - " embeddings = average_pool(outputs.last_hidden_state, batch_dict[\"attention_mask\"])\n", - "\n", - " # normalize embeddings\n", - " embeddings = F.normalize(embeddings, p=2, dim=1)\n", - " return embeddings.detach().numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69bb4501", - "metadata": {}, - "outputs": [], - "source": [ - "hf_embed(inputs=english_texts, model_id=model_id)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "451dbd16", - "metadata": {}, - "outputs": [], - "source": [ - "tokenizer = AutoTokenizer.from_pretrained(model_id)\n", - "model = ORTModelForFeatureExtraction.from_pretrained(model_id, export=True)\n", - "\n", - "# Remove all existing files in the save_dir using Path.unlink()\n", - "save_dir = Path(save_dir)\n", - "save_dir.mkdir(parents=True, exist_ok=True)\n", - "for p in save_dir.iterdir():\n", - " p.unlink()\n", - "\n", - "# Load the optimization configuration detailing the optimization we wish to apply\n", - "optimization_config = AutoOptimizationConfig.O4()\n", - "optimizer = ORTOptimizer.from_pretrained(model)\n", - "\n", - "optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config, use_external_data_format=True)\n", - "model = ORTModelForFeatureExtraction.from_pretrained(save_dir)\n", - "\n", - "tokenizer.save_pretrained(save_dir)\n", - "# model.save_pretrained(save_dir)\n", - "# model.push_to_hub(\"new_path_for_directory\", repository_id=\"my-onnx-repo\", use_auth_token=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8422cddd", - "metadata": {}, - "outputs": [], - "source": [ - "onnx_quant_embed = pipeline(\"feature-extraction\", model=model, accelerator=\"ort\", tokenizer=tokenizer,return_tensors=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51fa5775", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "embeddings = onnx_quant_embed(inputs=english_texts)\n", - "F.normalize(embeddings[4])[:,0], english_texts[4], len(embeddings), len(english_texts)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df405d70", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def measure_pipeline_time(pipeline, input_texts: List[str], num_runs=10, **kwargs: Any) -> Tuple[float, float]:\n", - " \"\"\"Measures the time it takes to run the pipeline on the input texts.\"\"\"\n", - " times = []\n", - " total_chars = sum(len(text) for text in input_texts)\n", - " for _ in range(num_runs):\n", - " start_time = time.time()\n", - " _ = pipeline(inputs=input_texts, **kwargs)\n", - " end_time = time.time()\n", - " times.append(end_time - start_time)\n", - "\n", - " mean_time = np.mean(times)\n", - " std_dev = np.std(times)\n", - " chars_per_second = total_chars / mean_time\n", - " return mean_time, std_dev, chars_per_second" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2d72aba5", - "metadata": {}, - "source": [ - "# Ours" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b881152", - "metadata": {}, - "outputs": [], - "source": [ - "_, _, chars_per_sec = measure_pipeline_time(onnx_quant_embed, input_texts)\n", - "print(f\"Multilingual Speed: {chars_per_sec:.2f} chars/sec\")\n", - "_, _, chars_per_sec = measure_pipeline_time(onnx_quant_embed, english_texts)\n", - "print(f\"English Speed: {chars_per_sec:.2f} chars/sec\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "49e1daf8", - "metadata": {}, - "source": [ - "# Original" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61b3bf53", - "metadata": {}, - "outputs": [], - "source": [ - "_, _, chars_per_sec = measure_pipeline_time(hf_embed, input_texts=input_texts, model_id=model_id)\n", - "print(f\"Multilingual Speed: {chars_per_sec:.2f} chars/sec\")\n", - "_, _, chars_per_sec = measure_pipeline_time(hf_embed, input_texts=english_texts, model_id=model_id)\n", - "print(f\"English Speed: {chars_per_sec:.2f} chars/sec\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f0b7da8f-ffe7-4f58-95dd-7e9836f19328", - "metadata": {}, - "source": [ - "# Compress & Upload\n", - "\n", - "## Compress" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "578b1d74", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from pathlib import Path\n", - "import tarfile\n", - "\n", - "save_dir = Path(\"../local_cache/fast-bge-small-en-v1.5\")\n", - "\n", - "def compress(directory_path):\n", - " directory_path = Path(directory_path)\n", - " assert directory_path.exists(), f\"{directory_path} does not exist\"\n", - " output_filename = directory_path.name + \".tar.gz\"\n", - " if Path(output_filename).exists():\n", - " print(\"We've an output file already? Manually delete that first\")\n", - " return output_filename\n", - "\n", - " with tarfile.open(output_filename, \"w:gz\") as tar:\n", - " tar.add(directory_path, arcname=os.path.basename(directory_path))\n", - " return output_filename\n", - "\n", - "\n", - "compressed_file_name = compress(save_dir)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "96cdf140-eca8-4778-9ebe-947988b4cfcb", - "metadata": {}, - "source": [ - "## Upload to Qdrant Google Cloud Storage" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1dab9595", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.9/site-packages/google/auth/_default.py:76: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a \"quota exceeded\" or \"API not enabled\" error. See the following page for troubleshooting: https://cloud.google.com/docs/authentication/adc-troubleshooting/user-creds. \n", - " warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File fast-bge-small-en-v1.5.tar.gz uploaded to qdrant-fastembed.\n" - ] - } - ], - "source": [ - "import os\n", - "from google.cloud import storage\n", - "\n", - "def upload(bucket_name, source_file_path):\n", - " storage_client = storage.Client(project=\"main\")\n", - " bucket = storage_client.bucket(bucket_name)\n", - " blob = bucket.blob(os.path.basename(source_file_path))\n", - "\n", - " blob.upload_from_filename(source_file_path)\n", - "\n", - " print(f\"File {source_file_path} uploaded to {bucket_name}.\")\n", - "\n", - "\n", - "upload(\"qdrant-fastembed\", source_file_path=compressed_file_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "731554f0", - "metadata": {}, - "outputs": [], - "source": [ - "# Remove the directory and compressed file\n", - "!rm -rvf {save_dir}\n", - "!rm -vf {save_dir}.tar.gz" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 38ecb33eff0db1beefce53a4b36147ed148e72ea Mon Sep 17 00:00:00 2001 From: Nirant Kasliwal Date: Mon, 15 Jan 2024 12:02:46 +0530 Subject: [PATCH 03/16] Add new dependencies: optimum, sentence-transformers, python-dotenv --- poetry.lock | 2611 +++++++++++++++++++++++++++++++++++++----------- pyproject.toml | 3 + 2 files changed, 2055 insertions(+), 559 deletions(-) diff --git a/poetry.lock b/poetry.lock index 26573b0f..0032dfac 100644 --- a/poetry.lock 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["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.8.0,<3.12" -content-hash = "b3be373f9f84bfe9791e04ff461d59df0ac2195233385d20c9cf083d56a89ce0" +content-hash = "94b4f675bc69985b5d65477e25f1520874b6a5badb34085893e74db7d95524a8" diff --git a/pyproject.toml b/pyproject.toml index 10bc1582..1f40775e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,6 +30,9 @@ mkdocstrings = "^0.22.0" pillow = "^10.0.0" cairosvg = "^2.7.1" mknotebooks = "^0.8.0" +optimum = "^1.16.1" +sentence-transformers = "^2.2.2" +python-dotenv = "^1.0.0" [build-system] From ee96fc7f64e2c0f83d60e540a126dce1baaa41e6 Mon Sep 17 00:00:00 2001 From: Nirant Kasliwal Date: Mon, 15 Jan 2024 12:34:18 +0530 Subject: [PATCH 04/16] Add jupyter-nbextensions-configurator package --- docs/examples/To_ONNX.ipynb | 143 ++++++++++++++++++++++-------------- poetry.lock | 61 ++++++++++++++- pyproject.toml | 1 + 3 files changed, 149 insertions(+), 56 deletions(-) diff --git a/docs/examples/To_ONNX.ipynb b/docs/examples/To_ONNX.ipynb index 5f4dc680..9e4517fb 100644 --- a/docs/examples/To_ONNX.ipynb +++ b/docs/examples/To_ONNX.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "0e9dbcde", "metadata": {}, "outputs": [], @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "id": "c37e1fda-c7f1-46e7-a5d4-19fa05c36ac1", "metadata": {}, "outputs": [], @@ -47,14 +47,14 @@ "from dotenv import load_dotenv\n", "\n", "load_dotenv() # take environment variables from .env.\n", - "from optimum.onnxruntime import AutoOptimizationConfig, ORTModelForFeatureExtraction, ORTOptimizer\n", + "from optimum.onnxruntime import AutoOptimizationConfig, ORTModelForFeatureExtraction, ORTOptimizer, ORTModel\n", "from optimum.pipelines import pipeline\n", "import torch.nn.functional as F" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "b1ecf0b6-db81-4da3-b47f-e31460ccfbf1", "metadata": {}, "outputs": [], @@ -63,12 +63,16 @@ "model_id = \"BAAI/bge-small-en-v1.5\"\n", "hf_model = AutoModel.from_pretrained(model_id)\n", "hf_tokenizer = AutoTokenizer.from_pretrained(model_id)\n", - "quantize = True" + "quantize = True\n", + "if quantize:\n", + " repository_id = f\"Qdrant/{model_id.split('/')[1]}-onnx-Q\"\n", + "else:\n", + " repository_id = f\"Qdrant/{model_id.split('/')[1]}-onnx\"" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "a38f5aed", "metadata": {}, "outputs": [], @@ -97,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "9f8c761c", "metadata": {}, "outputs": [], @@ -121,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "69bb4501", "metadata": {}, "outputs": [ @@ -140,7 +144,7 @@ " -0.01457597, 0.01262206]], dtype=float32)" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -159,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "451dbd16", "metadata": {}, "outputs": [ @@ -173,22 +177,20 @@ "Using framework PyTorch: 2.1.2\n", "Overriding 1 configuration item(s)\n", "\t- use_cache -> False\n", - "/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.9/site-packages/optimum/onnxruntime/configuration.py:770: FutureWarning: disable_embed_layer_norm will be deprecated soon, use disable_embed_layer_norm_fusion instead, disable_embed_layer_norm_fusion is set to True.\n", + "/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/optimum/onnxruntime/configuration.py:770: FutureWarning: disable_embed_layer_norm will be deprecated soon, use disable_embed_layer_norm_fusion instead, disable_embed_layer_norm_fusion is set to True.\n", " warnings.warn(\n", - "The argument use_external_data_format in the ORTOptimizer.optimize() method is deprecated and will be removed in optimum 2.0.\n", "Optimizing model...\n", - "There is no gpu for onnxruntime to do optimization.\n", - "Configuration saved in saved_models/ort_config.json\n", - "Optimized model saved at: saved_models (external data format: False; saved all tensor to one file: True)\n" + "Configuration saved in local_cache/ort_config.json\n", + "Optimized model saved at: local_cache (external data format: False; saved all tensor to one file: True)\n" ] }, { "data": { "text/plain": [ - "PosixPath('saved_models')" + "PosixPath('local_cache')" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -198,66 +200,99 @@ "\n", "# Load the optimization configuration detailing the optimization we wish to apply\n", "if quantize:\n", - " optimization_config = AutoOptimizationConfig.O4()\n", - " optimization_config = AutoOptimizationConfig.O4()\n", + " optimization_config = AutoOptimizationConfig.O3()\n", " optimizer = ORTOptimizer.from_pretrained(model)\n", "\n", - "save_dir = Path(\"saved_models\") # Path to save the optimized model, working directory\n", - "optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config, use_external_data_format=True)" + "save_dir = Path(\"local_cache\") # Path to save the optimized model, working directory\n", + "optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "id": "2ea5bd5a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Could not create tensor from given input list", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (5,) + inhomogeneous part.", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(repository_id)\n\u001b[1;32m 4\u001b[0m tokens \u001b[38;5;241m=\u001b[39m tokenizer(english_texts)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtokens\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/optimum/modeling_base.py:90\u001b[0m, in \u001b[0;36mOptimizedModel.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/optimum/onnxruntime/modeling_ort.py:958\u001b[0m, in \u001b[0;36mORTModelForFeatureExtraction.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, **kwargs)\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m token_type_ids \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 956\u001b[0m onnx_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_type_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m token_type_ids\n\u001b[0;32m--> 958\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43monnx_inputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 960\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_names[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlast_hidden_state\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m 961\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_torch:\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220\u001b[0m, in \u001b[0;36mSession.run\u001b[0;34m(self, output_names, input_feed, run_options)\u001b[0m\n\u001b[1;32m 218\u001b[0m output_names \u001b[38;5;241m=\u001b[39m [output\u001b[38;5;241m.\u001b[39mname \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_outputs_meta]\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 220\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_feed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m C\u001b[38;5;241m.\u001b[39mEPFail \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enable_fallback:\n", + "\u001b[0;31mRuntimeError\u001b[0m: Could not create tensor from given input list" + ] + } + ], "source": [ - "model = ORTModelForFeatureExtraction.from_pretrained(save_dir)" + "model = ORTModelForFeatureExtraction.from_pretrained(save_dir)\n", + "# optimized_model = AutoModel.from_pretrained(repository_id)\n", + "tokenizer = AutoTokenizer.from_pretrained(repository_id)\n", + "tokens = tokenizer(english_texts)\n", + "model(**tokens)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "68d7ab8b", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "402e707a28ea4b2baf6c7eea382933b7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "model_optimized.onnx: 0%| | 0.00/66.5M [00:00log for further details." + ] } ], "source": [ - "model.push_to_hub(save_directory=save_dir, repository_id=\"Qdrant/bge-small-en-v1.5-onnx-Q\", use_auth_token=True)" + "# model.push_to_hub(save_directory=save_dir, repository_id=repository_id, use_auth_token=True)" ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1da6a780", + "metadata": {}, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Could not create tensor from given input list", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (5,) + inhomogeneous part.", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(repository_id)\n\u001b[1;32m 3\u001b[0m tokens \u001b[38;5;241m=\u001b[39m tokenizer(english_texts)\n\u001b[0;32m----> 4\u001b[0m \u001b[43moptimized_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtokens\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/optimum/modeling_base.py:90\u001b[0m, in \u001b[0;36mOptimizedModel.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/optimum/onnxruntime/modeling_ort.py:958\u001b[0m, in \u001b[0;36mORTModelForFeatureExtraction.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, **kwargs)\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m token_type_ids \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 956\u001b[0m onnx_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_type_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m token_type_ids\n\u001b[0;32m--> 958\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43monnx_inputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 960\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_names[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlast_hidden_state\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m 961\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_torch:\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220\u001b[0m, in \u001b[0;36mSession.run\u001b[0;34m(self, output_names, input_feed, run_options)\u001b[0m\n\u001b[1;32m 218\u001b[0m output_names \u001b[38;5;241m=\u001b[39m [output\u001b[38;5;241m.\u001b[39mname \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_outputs_meta]\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 220\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_feed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m C\u001b[38;5;241m.\u001b[39mEPFail \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enable_fallback:\n", + "\u001b[0;31mRuntimeError\u001b[0m: Could not create tensor from given input list" + ] + } + ], + "source": [] } ], "metadata": { @@ -276,7 +311,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.17" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/poetry.lock b/poetry.lock index 0032dfac..ecd3ced3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1391,6 +1391,26 @@ traitlets = ">=5.3" docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] +[[package]] +name = "jupyter-contrib-core" +version = "0.4.2" +description = "Common utilities for jupyter-contrib projects." +optional = false +python-versions = "*" +files = [ + {file = "jupyter_contrib_core-0.4.2.tar.gz", hash = "sha256:1887212f3ca9d4487d624c0705c20dfdf03d5a0b9ea2557d3aaeeb4c38bdcabb"}, +] + +[package.dependencies] +jupyter_core = "*" +notebook = ">=4.0" +setuptools = "*" +tornado = "*" +traitlets = "*" + +[package.extras] +testing-utils = ["mock", "nose"] + [[package]] name = "jupyter-core" version = "5.7.1" @@ -1451,6 +1471,27 @@ files = [ importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""} jupyter-server = ">=1.1.2" +[[package]] +name = "jupyter-nbextensions-configurator" +version = "0.6.3" +description = "jupyter serverextension providing configuration interfaces for nbextensions." +optional = false +python-versions = "*" +files = [ + {file = "jupyter_nbextensions_configurator-0.6.3-py2.py3-none-any.whl", hash = "sha256:cece496f3f62cf80bb0b04867ea463c32ed5db19ff5814fe18a3a7f1bb9da95b"}, +] + +[package.dependencies] +jupyter-contrib-core = ">=0.3.3" +jupyter-core = "*" +notebook = ">=6.0" +pyyaml = "*" +tornado = "*" +traitlets = "*" + +[package.extras] +test = ["jupyter-contrib-core[testing-utils]", "mock", "nose", "requests", "selenium"] + [[package]] name = "jupyter-server" version = "2.12.4" @@ -2505,8 +2546,8 @@ files = [ [package.dependencies] numpy = [ {version = ">=1.20.3", markers = "python_version < \"3.10\""}, - {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""}, {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, + {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""}, ] python-dateutil = ">=2.8.2" pytz = ">=2020.1" @@ -3809,6 +3850,22 @@ files = [ {file = "sentencepiece-0.1.99.tar.gz", hash = "sha256:189c48f5cb2949288f97ccdb97f0473098d9c3dcf5a3d99d4eabe719ec27297f"}, ] +[[package]] +name = "setuptools" +version = "69.0.3" +description = "Easily download, build, install, upgrade, and uninstall Python packages" +optional = false +python-versions = ">=3.8" +files = [ + {file = "setuptools-69.0.3-py3-none-any.whl", hash = "sha256:385eb4edd9c9d5c17540511303e39a147ce2fc04bc55289c322b9e5904fe2c05"}, + {file = "setuptools-69.0.3.tar.gz", hash = "sha256:be1af57fc409f93647f2e8e4573a142ed38724b8cdd389706a867bb4efcf1e78"}, +] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] + [[package]] name = "six" version = "1.16.0" @@ -4696,4 +4753,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.8.0,<3.12" -content-hash = "94b4f675bc69985b5d65477e25f1520874b6a5badb34085893e74db7d95524a8" +content-hash = "4b282576eb3543110f9cd723bff072683a38d4d23ffc833a45b6aec6c9e22345" diff --git a/pyproject.toml b/pyproject.toml index 1f40775e..919371fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,6 +33,7 @@ mknotebooks = "^0.8.0" optimum = "^1.16.1" sentence-transformers = "^2.2.2" python-dotenv = "^1.0.0" +jupyter-nbextensions-configurator = "^0.6.3" [build-system] From 30fddf688d363d0ab1f81abbaddba6cec6805562 Mon Sep 17 00:00:00 2001 From: Nirant Kasliwal Date: Mon, 15 Jan 2024 12:34:23 +0530 Subject: [PATCH 05/16] Add local_cache to .gitignore --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 5e34b4d6..4d407210 100644 --- a/.gitignore +++ b/.gitignore @@ -176,4 +176,5 @@ fooling_around/fast-multilingual-e5-large/sentencepiece.bpe.model fooling_around/fast-multilingual-e5-large/special_tokens_map.json fooling_around/fast-multilingual-e5-large/tokenizer_config.json fooling_around/fast-multilingual-e5-large/tokenizer.json -docs/examples/saved_models/* \ No newline at end of file +docs/examples/saved_models/* +docs/examples/local_cache/* \ No newline at end of file From 126fdb0d31f905ced9af40ec4b25a77b46a2ed18 Mon Sep 17 00:00:00 2001 From: Nirant Kasliwal Date: Mon, 15 Jan 2024 12:51:07 +0530 Subject: [PATCH 06/16] Update optimum library version to 1.15 --- docs/examples/To_ONNX.ipynb | 194 ++++++++++++++++++++++-------------- poetry.lock | 12 +-- pyproject.toml | 2 +- 3 files changed, 128 insertions(+), 80 deletions(-) diff --git a/docs/examples/To_ONNX.ipynb b/docs/examples/To_ONNX.ipynb index 9e4517fb..6f6ea109 100644 --- a/docs/examples/To_ONNX.ipynb +++ b/docs/examples/To_ONNX.ipynb @@ -31,10 +31,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "c37e1fda-c7f1-46e7-a5d4-19fa05c36ac1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "import os\n", "from pathlib import Path\n", @@ -61,13 +70,16 @@ "source": [ "# Load the tokenizer and PyTorch model from HuggingFace Transformers\n", "model_id = \"BAAI/bge-small-en-v1.5\"\n", + "\n", "hf_model = AutoModel.from_pretrained(model_id)\n", "hf_tokenizer = AutoTokenizer.from_pretrained(model_id)\n", "quantize = True\n", "if quantize:\n", " repository_id = f\"Qdrant/{model_id.split('/')[1]}-onnx-Q\"\n", "else:\n", - " repository_id = f\"Qdrant/{model_id.split('/')[1]}-onnx\"" + " repository_id = f\"Qdrant/{model_id.split('/')[1]}-onnx\"\n", + "\n", + "save_dir = f\"local_cache/{repository_id}\"" ] }, { @@ -179,15 +191,21 @@ "\t- use_cache -> False\n", "/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/optimum/onnxruntime/configuration.py:770: FutureWarning: disable_embed_layer_norm will be deprecated soon, use disable_embed_layer_norm_fusion instead, disable_embed_layer_norm_fusion is set to True.\n", " warnings.warn(\n", + "The argument use_external_data_format in the ORTOptimizer.optimize() method is deprecated and will be removed in optimum 2.0.\n", "Optimizing model...\n", - "Configuration saved in local_cache/ort_config.json\n", - "Optimized model saved at: local_cache (external data format: False; saved all tensor to one file: True)\n" + "There is no gpu for onnxruntime to do optimization.\n", + "Configuration saved in local_cache/Qdrant/bge-small-en-v1.5-onnx-Q/ort_config.json\n", + "Optimized model saved at: local_cache/Qdrant/bge-small-en-v1.5-onnx-Q (external data format: False; saved all tensor to one file: True)\n" ] }, { "data": { "text/plain": [ - "PosixPath('local_cache')" + "('local_cache/Qdrant/bge-small-en-v1.5-onnx-Q/tokenizer_config.json',\n", + " 'local_cache/Qdrant/bge-small-en-v1.5-onnx-Q/special_tokens_map.json',\n", + " 'local_cache/Qdrant/bge-small-en-v1.5-onnx-Q/vocab.txt',\n", + " 'local_cache/Qdrant/bge-small-en-v1.5-onnx-Q/added_tokens.json',\n", + " 'local_cache/Qdrant/bge-small-en-v1.5-onnx-Q/tokenizer.json')" ] }, "execution_count": 7, @@ -196,103 +214,133 @@ } ], "source": [ + "tokenizer = AutoTokenizer.from_pretrained(model_id)\n", "model = ORTModelForFeatureExtraction.from_pretrained(model_id, export=True)\n", "\n", + "# Remove all existing files in the save_dir using Path.unlink()\n", + "save_dir = Path(save_dir)\n", + "save_dir.mkdir(parents=True, exist_ok=True)\n", + "for p in save_dir.iterdir():\n", + " p.unlink()\n", + "\n", "# Load the optimization configuration detailing the optimization we wish to apply\n", - "if quantize:\n", - " optimization_config = AutoOptimizationConfig.O3()\n", - " optimizer = ORTOptimizer.from_pretrained(model)\n", + "optimization_config = AutoOptimizationConfig.O4()\n", + "optimizer = ORTOptimizer.from_pretrained(model)\n", "\n", - "save_dir = Path(\"local_cache\") # Path to save the optimized model, working directory\n", - "optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config)" + "optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config, use_external_data_format=True)\n", + "model = ORTModelForFeatureExtraction.from_pretrained(save_dir)\n", + "tokenizer.save_pretrained(save_dir)" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "2ea5bd5a", + "execution_count": 10, + "id": "3587d3c4", "metadata": {}, "outputs": [ { - "ename": "RuntimeError", - "evalue": "Could not create tensor from given input list", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (5,) + inhomogeneous part.", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(repository_id)\n\u001b[1;32m 4\u001b[0m tokens \u001b[38;5;241m=\u001b[39m tokenizer(english_texts)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtokens\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/optimum/modeling_base.py:90\u001b[0m, in \u001b[0;36mOptimizedModel.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/optimum/onnxruntime/modeling_ort.py:958\u001b[0m, in \u001b[0;36mORTModelForFeatureExtraction.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, **kwargs)\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m token_type_ids \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 956\u001b[0m onnx_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_type_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m token_type_ids\n\u001b[0;32m--> 958\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43monnx_inputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 960\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_names[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlast_hidden_state\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m 961\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_torch:\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220\u001b[0m, in \u001b[0;36mSession.run\u001b[0;34m(self, output_names, input_feed, run_options)\u001b[0m\n\u001b[1;32m 218\u001b[0m output_names \u001b[38;5;241m=\u001b[39m [output\u001b[38;5;241m.\u001b[39mname \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_outputs_meta]\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 220\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_feed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m C\u001b[38;5;241m.\u001b[39mEPFail \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enable_fallback:\n", - "\u001b[0;31mRuntimeError\u001b[0m: Could not create tensor from given input list" + "name": "stderr", + "output_type": "stream", + "text": [ + "model_optimized.onnx: 100%|██████████| 66.5M/66.5M [00:09<00:00, 7.37MB/s]\n" ] } ], "source": [ - "model = ORTModelForFeatureExtraction.from_pretrained(save_dir)\n", - "# optimized_model = AutoModel.from_pretrained(repository_id)\n", - "tokenizer = AutoTokenizer.from_pretrained(repository_id)\n", - "tokens = tokenizer(english_texts)\n", - "model(**tokens)" + "model.push_to_hub(save_directory=save_dir, repository_id=repository_id, use_auth_token=True)" + ] + }, + { + "cell_type": "markdown", + "id": "fde9f7a3", + "metadata": {}, + "source": [ + "## Trying out the model from Huggingface Hub" ] }, { "cell_type": "code", - "execution_count": null, - "id": "68d7ab8b", + "execution_count": 14, + "id": "be18d371", "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mnotebook controller is DISPOSED. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mnotebook controller is DISPOSED. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], + "outputs": [], "source": [ - "# model.push_to_hub(save_directory=save_dir, repository_id=repository_id, use_auth_token=True)" + "onnx_model = ORTModelForFeatureExtraction.from_pretrained(repository_id)\n", + "onnx_tokenizer = AutoTokenizer.from_pretrained(repository_id)" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "1da6a780", + "execution_count": 15, + "id": "532bd348", "metadata": {}, "outputs": [ { - "ename": "RuntimeError", - "evalue": "Could not create tensor from given input list", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (5,) + inhomogeneous part.", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[9], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(repository_id)\n\u001b[1;32m 3\u001b[0m tokens \u001b[38;5;241m=\u001b[39m tokenizer(english_texts)\n\u001b[0;32m----> 4\u001b[0m \u001b[43moptimized_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtokens\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/optimum/modeling_base.py:90\u001b[0m, in \u001b[0;36mOptimizedModel.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/optimum/onnxruntime/modeling_ort.py:958\u001b[0m, in \u001b[0;36mORTModelForFeatureExtraction.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, **kwargs)\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m token_type_ids \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 956\u001b[0m onnx_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_type_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m token_type_ids\n\u001b[0;32m--> 958\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43monnx_inputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 960\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_names[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlast_hidden_state\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m 961\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_torch:\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/fst/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220\u001b[0m, in \u001b[0;36mSession.run\u001b[0;34m(self, output_names, input_feed, run_options)\u001b[0m\n\u001b[1;32m 218\u001b[0m output_names \u001b[38;5;241m=\u001b[39m [output\u001b[38;5;241m.\u001b[39mname \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_outputs_meta]\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 220\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_names\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_feed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m C\u001b[38;5;241m.\u001b[39mEPFail \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enable_fallback:\n", - "\u001b[0;31mRuntimeError\u001b[0m: Could not create tensor from given input list" - ] + "data": { + "text/plain": [ + "(tensor([[-0.2167, 0.0514, 0.0928, 0.1594, 0.2467, 0.3481, -0.0795, 0.1916,\n", + " 0.2227, -0.1297, 0.2020, -0.1873, 0.2221, 0.3651, 0.2194, -0.0692,\n", + " 0.1239, 0.2137, 0.0195, -0.2582, 0.2084, -0.1736, -0.0366, -0.2664,\n", + " -0.2339, 0.2233, -0.0657, -0.2686, -0.2866, -0.2278, 0.0309, 0.0677,\n", + " 0.2661, 0.1537, -0.0069, -0.3319, -0.3038, 0.2219, 0.3027, -0.2240,\n", + " -0.0523, 0.1749, -0.2705, 0.1487, -0.3244, -0.2069, -0.2114, -0.1821,\n", + " -0.1516, 0.2255, -0.2053, -0.2625, -0.0964, 0.3533, 0.2315, 0.1583,\n", + " 0.2405, -0.1198, -0.2908, 0.0707, 0.1949, 0.2105, -0.1731, 0.2771,\n", + " 0.2203, -0.1494, 0.0959, -0.1590, -0.1761, -0.0311, 0.3467, 0.2385,\n", + " 0.0964, 0.1245, 0.0470, -0.1691, -0.1228, -0.2064, -0.1982, -0.2398,\n", + " 0.0165, 0.0306, -0.1663, -0.0887, -0.1120, -0.2306, 0.1256, -0.2352,\n", + " 0.1686, -0.4168, -0.1018, -0.1619, -0.1757, -0.3001, -0.2155, -0.2885,\n", + " 0.1868, 0.1945, 0.0881, 0.6445, -0.3311, 0.1964, -0.0292, -0.2312,\n", + " -0.0040, -0.2386, -0.2235, -0.2751, -0.1402, -0.1766, 0.2363, -0.4445,\n", + " 0.2357, -0.0622, -0.0413, -0.0783, 0.1433, 0.2122, 0.2078, 0.1495,\n", + " -0.3446, 0.1067, 0.1613, 0.2959, 0.2626, -0.4740, -0.1082, 0.3239,\n", + " -0.1803, 0.0997, -0.2434, 0.2762, -0.2583, 0.3141, 0.0112, 0.1745,\n", + " 0.2053, -0.1768, -0.1194, 0.0859, -0.3041, -0.1982, 0.2145, -0.0532,\n", + " -0.2455, 0.2052, -0.1348, -0.2428, -0.2488, -0.2168, 0.2666, 0.2777,\n", + " -0.1524, -0.2402, 0.0133, 0.2093, 0.2458, -0.1074, -0.2419, -0.2991,\n", + " -0.1752, 0.0802, -0.2866, 0.2294, -0.2542, -0.2342, 0.3443, -0.3247,\n", + " -0.1445, -0.0290, 0.1546, 0.0302, -0.1828, 0.2438, 0.0751, 0.0462,\n", + " -0.1671, -0.3660, -0.0694, -0.0095, 0.1836, -0.1069, 0.0990, 0.2472,\n", + " 0.0868, -0.0425, 0.2824, -0.2537, 0.1205, 0.1889, -0.2124, 0.0136,\n", + " -0.1349, 0.0912, -0.0262, -0.2412, 0.2757, 0.2797, 0.0145, -0.2441,\n", + " 0.2213, -0.0217, -0.1446, 0.2326, 0.0941, 0.1203, -0.1906, -0.1431,\n", + " -0.0583, 0.2193, 0.1849, -0.0185, 0.0284, -0.1525, 0.2094, -0.1811,\n", + " 0.2608, 0.2625, -0.0797, 0.2194, -0.1662, -0.0763, -0.1854, -0.3313,\n", + " -0.1781, -0.1981, 0.1939, -0.2452, -0.2262, -0.0456, -0.1490, 0.2020,\n", + " 0.1871, -0.1022, 0.2299, -0.2127, 0.1315, 0.0536, -0.0557, 0.1494,\n", + " 0.2217, -0.3015, -0.3018, 0.1369, 0.1541, 0.2631, -0.2188, 0.1107,\n", + " -0.1987, 0.2343, 0.0984, -0.2731, -0.1080, -0.0538, -0.2362, -0.2376,\n", + " -0.1168, 0.2176, 0.0687, 0.0626, 0.1006, -0.1661, -0.1415, -0.1248,\n", + " -0.2594, 0.2187, -0.1395, -0.2153, -0.4498, -0.1936, -0.1451, -0.1676,\n", + " 0.2641, -0.1155, -0.0400, 0.2400, -0.3057, -0.0794, -0.2021, 0.0650,\n", + " 0.1192, -0.0939, 0.1083, 0.1714, -0.2247, -0.3154, -0.1720, 0.1512,\n", + " -0.2241, -0.3141, 0.2288, -0.1172, -0.1860, -0.2495, 0.0397, -0.0322,\n", + " 0.2313, 0.2013, 0.0296, 0.2663, 0.0793, 0.2103, -0.1596, 0.2250,\n", + " 0.1019, 0.2159, -0.2309, 0.1712, 0.2584, 0.1692, -0.0331, 0.1986,\n", + " 0.2638, 0.1509, 0.0965, -0.1657, -0.1721, 0.2107, 0.2588, -0.2725,\n", + " 0.1890, -0.0129, 0.0519, -0.1113, 0.2591, 0.2191, -0.2598, 0.2012,\n", + " 0.1182, -0.0599, 0.1190, 0.1726, -0.2416, -0.1674, -0.0343, 0.1782,\n", + " 0.0531, 0.3363, -0.0606, 0.1282, 0.3418, 0.2447, -0.3291, 0.1275,\n", + " -0.0873, 0.0312, -0.2825, -0.1609, -0.1207, -0.3035, -0.2454, 0.2148,\n", + " -0.2081, 0.0118, 0.2411, 0.2439, 0.1598, -0.3169, -0.2042, 0.2784,\n", + " 0.1087, -0.1713, 0.2854, 0.1956, 0.1949, 0.3777, -0.3244, 0.1883,\n", + " 0.0507, -0.1668, -0.0155, 0.0348, 0.1672, -0.1819, -0.1733, -0.1864,\n", + " -0.2057, 0.0486, -0.1979, 0.0774, -0.0145, -0.0991, -0.2121, 0.0822]]),\n", + " 'Qdrant: The ultimate tool for high-dimensional indexing and search.',\n", + " 5,\n", + " 5)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" } ], - "source": [] + "source": [ + "onnx_quant_embed = pipeline(\"feature-extraction\", model=onnx_model, accelerator=\"ort\", tokenizer=onnx_tokenizer,return_tensors=True)\n", + "embeddings = onnx_quant_embed(inputs=english_texts)\n", + "F.normalize(embeddings[4])[:,0], english_texts[4], len(embeddings), len(english_texts)" + ] } ], "metadata": { diff --git a/poetry.lock b/poetry.lock index ecd3ced3..9eb6632e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2435,13 +2435,13 @@ sympy = "*" [[package]] name = "optimum" -version = "1.16.1" +version = "1.15.0" description = "Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality." optional = false python-versions = ">=3.7.0" files = [ - {file = "optimum-1.16.1-py3-none-any.whl", hash = "sha256:c0d0cb5877ae7b8f40289c54885341297f61b30255a93a71f5e979bc86b03598"}, - {file = "optimum-1.16.1.tar.gz", hash = "sha256:9a74bc5cbbe2373fc45f477257d5046fd28b725bce91c1f8846baadba57f2129"}, + {file = "optimum-1.15.0-py3-none-any.whl", hash = "sha256:3639673740e28989aff075dcfbb1971f1492044894f4210b947d9b32eb7b9809"}, + {file = "optimum-1.15.0.tar.gz", hash = "sha256:3cf16b1c04a4ac4e6c4175df921a05deac383f0150b0e4acab102180f610e7db"}, ] [package.dependencies] @@ -2457,7 +2457,7 @@ transformers = {version = ">=4.26.0", extras = ["sentencepiece"]} [package.extras] amd = ["optimum-amd"] benchmark = ["evaluate (>=0.2.0)", "optuna", "scikit-learn", "seqeval", "torchvision", "tqdm"] -dev = ["Pillow", "accelerate", "black (>=23.1,<24.0)", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest", "pytest-xdist", "requests", "ruff (==0.1.5)", "sacremoses", "torchaudio", "torchvision"] +dev = ["Pillow", "accelerate", "black (>=23.1,<24.0)", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest", "pytest-xdist", "requests", "ruff (>=0.0.241,<=0.0.259)", "sacremoses", "torchaudio", "torchvision"] diffusers = ["diffusers"] doc-build = ["accelerate"] exporters = ["onnx", "onnxruntime", "timm"] @@ -2474,7 +2474,7 @@ nncf = ["optimum-intel[nncf] (>=1.12.0)"] onnxruntime = ["datasets (>=1.2.1)", "evaluate", "onnx", "onnxruntime (>=1.11.0)", "protobuf (>=3.20.1)"] onnxruntime-gpu = ["accelerate", "datasets (>=1.2.1)", "evaluate", "onnx", "onnxruntime-gpu (>=1.11.0)", "protobuf (>=3.20.1)"] openvino = ["optimum-intel[openvino] (>=1.12.0)"] -quality = ["black (>=23.1,<24.0)", "ruff (==0.1.5)"] +quality = ["black (>=23.1,<24.0)", "ruff (>=0.0.241,<=0.0.259)"] tests = ["Pillow", "accelerate", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest", "pytest-xdist", "requests", "sacremoses", "torchaudio", "torchvision"] [[package]] @@ -4753,4 +4753,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.8.0,<3.12" -content-hash = "4b282576eb3543110f9cd723bff072683a38d4d23ffc833a45b6aec6c9e22345" +content-hash = "e1177c0bbc418d8b684f8a36873a1b2a101ceb1b98fb7d69d600cb477b448f2b" diff --git a/pyproject.toml b/pyproject.toml index 919371fd..5331479d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,7 +30,7 @@ mkdocstrings = "^0.22.0" pillow = "^10.0.0" cairosvg = "^2.7.1" mknotebooks = "^0.8.0" -optimum = "^1.16.1" +optimum = "1.15" sentence-transformers = "^2.2.2" python-dotenv = "^1.0.0" jupyter-nbextensions-configurator = "^0.6.3" From e17421634eb37cea6ef2f47822907b10144c1cc3 Mon Sep 17 00:00:00 2001 From: Nirant Kasliwal Date: Mon, 15 Jan 2024 13:04:30 +0530 Subject: [PATCH 07/16] Update installation instructions in To_ONNX.ipynb --- docs/examples/To_ONNX.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/examples/To_ONNX.ipynb b/docs/examples/To_ONNX.ipynb index 6f6ea109..58f2876f 100644 --- a/docs/examples/To_ONNX.ipynb +++ b/docs/examples/To_ONNX.ipynb @@ -15,7 +15,9 @@ "\n", "```bash\n", "poetry install\n", - "```" + "```\n", + "\n", + "Optimum is not backward compatible, so do not upgrade to the latest version. Instead, use the versions pinned in `pyproject.toml`." ] }, { From 3c62ee7283e27291df387e9008665f339f4140a7 Mon Sep 17 00:00:00 2001 From: Nirant Kasliwal Date: Wed, 17 Jan 2024 10:59:49 +0530 Subject: [PATCH 08/16] Update dependencies in pyproject.toml --- pyproject.toml | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 5331479d..fb7a796f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,9 +21,7 @@ huggingface-hub = "0.19.4" [tool.poetry.group.dev.dependencies] pytest = "^7.4.2" -ruff = "^0.0.277" -isort = "^5.12.0" -black = "^23.7.0" +ruff = "^0.1.13" notebook = ">=7.0.2" mkdocs-material = "^9.1.21" mkdocstrings = "^0.22.0" @@ -40,11 +38,5 @@ jupyter-nbextensions-configurator = "^0.6.3" requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" -[tool.black] -line-length = 120 - -[tool.isort] -profile = "black" - [tool.ruff] line-length = 120 From 559854f76dae37eb820121c495a18ab174233bd5 Mon Sep 17 00:00:00 2001 From: Nirant Kasliwal Date: Wed, 17 Jan 2024 11:00:34 +0530 Subject: [PATCH 09/16] Update poetry lock --- poetry.lock | 151 ++++++++++++++-------------------------------------- 1 file changed, 40 insertions(+), 111 deletions(-) diff --git a/poetry.lock b/poetry.lock index 9eb6632e..aac1a0c3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -327,52 +327,6 @@ soupsieve = ">1.2" html5lib = ["html5lib"] lxml = ["lxml"] -[[package]] -name = "black" -version = "23.12.1" -description = "The uncompromising code formatter." -optional = false -python-versions = ">=3.8" -files = [ - 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- master + - master - main permissions: contents: write @@ -14,7 +14,7 @@ jobs: - uses: actions/setup-python@v4 with: python-version: 3.x - - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV + - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV - uses: actions/cache@v3 with: key: mkdocs-material-${{ env.cache_id }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 9968eebd..0f4b73ab 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,12 +1,9 @@ repos: -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v3.2.0 - hooks: - - id: trailing-whitespace - - id: end-of-file-fixer - - id: check-yaml - - id: check-added-large-files -- repo: https://github.com/psf/black - rev: 23.7.0 - hooks: - - id: black + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.1.13 + hooks: + - id: ruff + types_or: [ python, pyi, jupyter ] + args: [ --fix ] + - id: ruff-format + types_or: [ python, pyi, jupyter ] diff --git a/README.md b/README.md index b4826d7b..27e3cfdb 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ The default embedding supports "query" and "passage" prefixes for the input text ## 🚀 Installation -To install the FastEmbed library, pip works: +To install the FastEmbed library, pip works: ```bash pip install fastembed @@ -36,8 +36,8 @@ documents: List[str] = [ "passage: This is an example passage.", "fastembed is supported by and maintained by Qdrant." # You can leave out the prefix but it's recommended ] -embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512) -embeddings: List[np.ndarray] = list(embedding_model.embed(documents)) # Note the list() call - this is a generator +embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512) +embeddings: List[np.ndarray] = list(embedding_model.embed(documents)) # Note the list() call - this is a generator ``` ## Usage with Qdrant @@ -48,7 +48,7 @@ Installation with Qdrant Client in Python: pip install qdrant-client[fastembed] ``` -Might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh. +Might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh. ```python from qdrant_client import QdrantClient diff --git a/docs/examples/FastEmbed_vs_HF_Comparison.ipynb b/docs/examples/FastEmbed_vs_HF_Comparison.ipynb index d842cb82..5b7a9eac 100644 --- a/docs/examples/FastEmbed_vs_HF_Comparison.ipynb +++ b/docs/examples/FastEmbed_vs_HF_Comparison.ipynb @@ -20,10 +20,8 @@ "outputs": [], "source": [ "import time\n", - "from pathlib import Path\n", - "from typing import Any, Callable, List, Tuple\n", + "from typing import Callable, List, Tuple\n", "\n", - "import numpy as np\n", "import torch.nn.functional as F\n", "from fastembed.embedding import DefaultEmbedding\n", "import matplotlib.pyplot as plt\n", @@ -116,6 +114,7 @@ " sentence_embeddings = F.normalize(sentence_embeddings)\n", " return sentence_embeddings\n", "\n", + "\n", "hf = HF(model_id=\"BAAI/bge-small-en-v1.5\")\n", "hf.embed(documents).shape" ] @@ -165,6 +164,8 @@ ], "source": [ "import types\n", + "\n", + "\n", "def calculate_time_stats(embed_func: Callable, documents: list, k: int) -> Tuple[float, float, float]:\n", " times = []\n", " for _ in range(k):\n", @@ -181,6 +182,7 @@ " # Returning mean, max, and min time for the call\n", " return (sum(times) / k, max(times), min(times))\n", "\n", + "\n", "hf_stats = calculate_time_stats(hf.embed, documents, k=2)\n", "print(f\"Huggingface Transformers (Average, Max, Min): {hf_stats}\")\n", "fst_stats = calculate_time_stats(embedding_model.embed, documents, k=2)\n", @@ -289,6 +291,7 @@ " \"\"\"\n", " return F.cosine_similarity(embeddings1, embeddings2).mean().item()\n", "\n", + "\n", "calculate_cosine_similarity(hf.embed(documents), Tensor(list(embedding_model.embed(documents))))" ] }, diff --git a/docs/examples/Supported_Models.ipynb b/docs/examples/Supported_Models.ipynb index df9bccd7..18b64899 100644 --- a/docs/examples/Supported_Models.ipynb +++ b/docs/examples/Supported_Models.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -141,12 +151,10 @@ } ], "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", "from fastembed.embedding import Embedding\n", "import pandas as pd\n", - "pd.set_option('display.max_colwidth', None)\n", + "\n", + "pd.set_option(\"display.max_colwidth\", None)\n", "pd.DataFrame(Embedding.list_supported_models())" ] } diff --git a/docs/examples/Usage_With_Qdrant.ipynb b/docs/examples/Usage_With_Qdrant.ipynb index d60095a3..91d79861 100644 --- a/docs/examples/Usage_With_Qdrant.ipynb +++ b/docs/examples/Usage_With_Qdrant.ipynb @@ -47,8 +47,6 @@ "outputs": [], "source": [ "from typing import List\n", - "import numpy as np\n", - "from fastembed.embedding import FlagEmbedding as Embedding\n", "from qdrant_client import QdrantClient" ] }, @@ -170,12 +168,7 @@ "ids = [42, 2]\n", "\n", "# Use the new add method\n", - "client.add(\n", - " collection_name=\"demo_collection\",\n", - " documents=docs,\n", - " metadata=metadata,\n", - " ids=ids\n", - ")" + "client.add(collection_name=\"demo_collection\", documents=docs, metadata=metadata, ids=ids)" ] }, { @@ -199,10 +192,7 @@ } ], "source": [ - "search_result = client.query(\n", - " collection_name=\"demo_collection\",\n", - " query_text=[\"This is a query document\"]\n", - ")\n", + "search_result = client.query(collection_name=\"demo_collection\", query_text=[\"This is a query document\"])\n", "print(search_result)" ] }, diff --git a/docs/experimental/Binary Quantization from Scratch.ipynb b/docs/experimental/Binary Quantization from Scratch.ipynb index cf491a9c..cb4f5c8d 100644 --- a/docs/experimental/Binary Quantization from Scratch.ipynb +++ b/docs/experimental/Binary Quantization from Scratch.ipynb @@ -46,7 +46,6 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "from tqdm import tqdm" ] }, @@ -305,9 +304,11 @@ "sampling_rate = [1, 2, 3, 5]\n", "results = []\n", "\n", + "\n", "def mean_accuracy(number_of_samples, limit, sampling_rate):\n", " return np.mean([accuracy(i, limit=limit, oversampling=sampling_rate) for i in range(number_of_samples)])\n", "\n", + "\n", "for i in tqdm(sampling_rate):\n", " for j in tqdm(limits):\n", " result = {\"sampling_rate\": i, \"limit\": j, \"recall\": mean_accuracy(number_of_samples, j, i)}\n", diff --git a/docs/experimental/Binary Quantization with Qdrant.ipynb b/docs/experimental/Binary Quantization with Qdrant.ipynb index 956943a8..89e11eea 100644 --- a/docs/experimental/Binary Quantization with Qdrant.ipynb +++ b/docs/experimental/Binary Quantization with Qdrant.ipynb @@ -34,10 +34,8 @@ "outputs": [], "source": [ "import pandas as pd\n", - "import uuid\n", "from qdrant_client import QdrantClient\n", - "from qdrant_client.http import models\n", - "from qdrant_client.http.models import PointStruct" + "from qdrant_client.http import models" ] }, { @@ -71,6 +69,7 @@ ], "source": [ "import datasets\n", + "\n", "dataset = datasets.load_dataset(\"KShivendu/dbpedia-entities-openai-1M\", split=\"train[0:100000]\")" ] }, @@ -133,10 +132,8 @@ } ], "source": [ - "from qdrant_client import QdrantClient\n", - "\n", "# client = QdrantClient(\n", - "# url=\"https://2aaa9439-b209-4ba6-8beb-d0b61dbd9388.us-east-1-0.aws.cloud.qdrant.io:6333\", \n", + "# url=\"https://2aaa9439-b209-4ba6-8beb-d0b61dbd9388.us-east-1-0.aws.cloud.qdrant.io:6333\",\n", "# api_key=\"FCF8_ADVuSRrtNGeg_rBJvAMJecEDgQhzuXMZGW8F7OzvaC9wYOPeQ\",\n", "# prefer_grpc=True\n", "# )\n", @@ -175,12 +172,10 @@ "bs = 10000\n", "for i in range(0, len(dataset), bs):\n", " client.upload_collection(\n", - " collection_name=collection_name, \n", - " ids=range(i, i+bs),\n", - " vectors=dataset[i:i+bs][\"openai\"],\n", - " payload=[\n", - " {\"text\": x} for x in dataset[i:i+bs][\"text\"]\n", - " ],\n", + " collection_name=collection_name,\n", + " ids=range(i, i + bs),\n", + " vectors=dataset[i : i + bs][\"openai\"],\n", + " payload=[{\"text\": x} for x in dataset[i : i + bs][\"text\"]],\n", " parallel=10,\n", " )" ] @@ -203,10 +198,7 @@ ], "source": [ "client.update_collection(\n", - " collection_name=f\"{collection_name}\",\n", - " optimizer_config=models.OptimizersConfigDiff(\n", - " indexing_threshold=20000\n", - " )\n", + " collection_name=f\"{collection_name}\", optimizer_config=models.OptimizersConfigDiff(indexing_threshold=20000)\n", ")" ] }, @@ -289,6 +281,7 @@ "source": [ "import random\n", "from random import randint\n", + "\n", "random.seed(37)\n", "\n", "query_indices = [randint(0, len(dataset)) for _ in range(100)]\n", @@ -304,7 +297,10 @@ "source": [ "## Add Gaussian noise to any vector\n", "import numpy as np\n", + "\n", "np.random.seed(37)\n", + "\n", + "\n", "def add_noise(vector, noise=0.05):\n", " return vector + noise * np.random.randn(*vector.shape)" ] @@ -959,6 +955,8 @@ ], "source": [ "import time\n", + "\n", + "\n", "def correct(results, text):\n", " result_texts = [x.payload[\"text\"] for x in results]\n", " return text in result_texts\n", @@ -977,7 +975,7 @@ " rescore=rescore,\n", " oversampling=oversampling,\n", " )\n", - " )\n", + " ),\n", " )\n", " correct_results += correct(results, text)\n", " return correct_results\n", @@ -996,14 +994,16 @@ " start = time.time()\n", " correct_results = count_correct(query_dataset, limit=limit, oversampling=oversampling, rescore=rescore)\n", " end = time.time()\n", - " results.append({\n", - " \"limit\": limit,\n", - " \"oversampling\": oversampling,\n", - " \"rescore\": rescore,\n", - " \"correct\": correct_results,\n", - " \"total queries\": len(query_dataset[\"text\"]),\n", - " \"time\": end - start,\n", - " })\n", + " results.append(\n", + " {\n", + " \"limit\": limit,\n", + " \"oversampling\": oversampling,\n", + " \"rescore\": rescore,\n", + " \"correct\": correct_results,\n", + " \"total queries\": len(query_dataset[\"text\"]),\n", + " \"time\": end - start,\n", + " }\n", + " )\n", "\n", "results_df = pd.DataFrame(results)\n", "results_df" diff --git a/docs/index.md b/docs/index.md index 90652e1b..969b1a5d 100644 --- a/docs/index.md +++ b/docs/index.md @@ -15,7 +15,7 @@ The default embedding supports "query" and "passage" prefixes for the input text ## 🚀 Installation -To install the FastEmbed library, pip works: +To install the FastEmbed library, pip works: ```bash pip install fastembed @@ -32,8 +32,8 @@ documents: List[str] = [ "passage: This is an example passage.", "fastembed is supported by and maintained by Qdrant." # You can leave out the prefix but it's recommended ] -embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512) -embeddings: List[np.ndarray] = embedding_model.embed(documents) # If you use +embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512) +embeddings: List[np.ndarray] = embedding_model.embed(documents) # If you use ``` ## Usage with Qdrant @@ -44,7 +44,7 @@ Installation with Qdrant Client in Python: pip install qdrant-client[fastembed] ``` -Might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh. +Might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh. ```python from qdrant_client import QdrantClient @@ -73,4 +73,4 @@ search_result = client.query( query_text="This is a query document" ) print(search_result) -``` \ No newline at end of file +``` diff --git a/docs/overrides/main.html b/docs/overrides/main.html index 5f111b35..1c0fd5b4 100644 --- a/docs/overrides/main.html +++ b/docs/overrides/main.html @@ -5,7 +5,7 @@ -{% endif %} +{% endif %} {{ super() }} @@ -24,4 +24,4 @@ href="https://cloud.qdrant.io?utm_source=twitter&utm_medium=website&utm_campaign=fastembed">Qdrant Cloud to get started with vector search! -{% endblock %} \ No newline at end of file +{% endblock %} diff --git a/fastembed/embedding.py b/fastembed/embedding.py index f642c65e..2edd6797 100644 --- a/fastembed/embedding.py +++ b/fastembed/embedding.py @@ -3,7 +3,6 @@ import shutil import tarfile import tempfile - from abc import ABC, abstractmethod from itertools import islice from multiprocessing import get_all_start_methods @@ -76,11 +75,11 @@ def load_tokenizer(cls, model_dir: Path, max_length: int = 512) -> Tokenizer: return tokenizer def __init__( - self, - path: Path, - model_name: str, - max_length: int = 512, - max_threads: int = None, + self, + path: Path, + model_name: str, + max_length: int = 512, + max_threads: int = None, ): self.path = path self.model_name = model_name @@ -134,10 +133,10 @@ def onnx_embed(self, documents: List[str]) -> Tuple[np.ndarray, np.ndarray]: class EmbeddingWorker(Worker): def __init__( - self, - path: Path, - model_name: str, - max_length: int = 512, + self, + path: Path, + model_name: str, + max_length: int = 512, ): self.model = EmbeddingModel(path=path, model_name=model_name, max_length=max_length, max_threads=1) @@ -189,56 +188,56 @@ def list_supported_models(cls) -> List[Dict[str, Union[str, Union[int, float]]]] "model": "BAAI/bge-small-en", "dim": 384, "description": "Fast English model", - "size_in_GB": 0.2 + "size_in_GB": 0.2, }, { "model": "BAAI/bge-small-en-v1.5", "dim": 384, "description": "Fast and Default English model", - "size_in_GB": 0.13 + "size_in_GB": 0.13, }, { "model": "BAAI/bge-small-zh-v1.5", "dim": 512, "description": "Fast and recommended Chinese model", - "size_in_GB": 0.1 + "size_in_GB": 0.1, }, { "model": "BAAI/bge-base-en", "dim": 768, "description": "Base English model", - "size_in_GB": 0.5 + "size_in_GB": 0.5, }, { "model": "BAAI/bge-base-en-v1.5", "dim": 768, "description": "Base English model, v1.5", - "size_in_GB": 0.44 + "size_in_GB": 0.44, }, { "model": "sentence-transformers/all-MiniLM-L6-v2", "dim": 384, "description": "Sentence Transformer model, MiniLM-L6-v2", - "size_in_GB": 0.09 + "size_in_GB": 0.09, }, { "model": "intfloat/multilingual-e5-large", "dim": 1024, "description": "Multilingual model, e5-large. Recommend using this model for non-English languages", - "size_in_GB": 2.24 + "size_in_GB": 2.24, }, { "model": "jinaai/jina-embeddings-v2-base-en", "dim": 768, "description": " English embedding model supporting 8192 sequence length", - "size_in_GB": 0.55 + "size_in_GB": 0.55, }, { "model": "jinaai/jina-embeddings-v2-small-en", "dim": 512, "description": " English embedding model supporting 8192 sequence length", - "size_in_GB": 0.13 - } + "size_in_GB": 0.13, + }, ] @classmethod @@ -310,7 +309,9 @@ def download_files_from_huggingface(cls, repod_id: str, cache_dir: Optional[str] from huggingface_hub import snapshot_download return snapshot_download( - repo_id=repod_id, ignore_patterns=["model.safetensors", "pytorch_model.bin"], cache_dir=cache_dir + repo_id=repod_id, + ignore_patterns=["model.safetensors", "pytorch_model.bin"], + cache_dir=cache_dir, ) @classmethod @@ -405,7 +406,7 @@ def retrieve_model_hf(self, model_name: str, cache_dir: str) -> Path: """ assert ( - "/" in model_name + "/" in model_name ), "model_name must be in the format / e.g. jinaai/jina-embeddings-v2-small-en" return Path(self.download_files_from_huggingface(repod_id=model_name, cache_dir=cache_dir)) @@ -451,11 +452,11 @@ class FlagEmbedding(Embedding): """ def __init__( - self, - model_name: str = "BAAI/bge-small-en-v1.5", - max_length: int = 512, - cache_dir: str = None, - threads: int = None, + self, + model_name: str = "BAAI/bge-small-en-v1.5", + max_length: int = 512, + cache_dir: str = None, + threads: int = None, ): """ Args: @@ -480,11 +481,10 @@ def __init__( self._model_dir = self.retrieve_model_gcs(model_name, cache_dir) self._max_length = max_length - self.model = EmbeddingModel(self._model_dir, self.model_name, max_length=max_length, - max_threads=threads) + self.model = EmbeddingModel(self._model_dir, self.model_name, max_length=max_length, max_threads=threads) def embed( - self, documents: Union[str, Iterable[str]], batch_size: int = 256, parallel: int = None + self, documents: Union[str, Iterable[str]], batch_size: int = 256, parallel: int = None ) -> Iterable[np.ndarray]: """ Encode a list of documents into list of embeddings. @@ -536,7 +536,7 @@ def list_supported_models(cls) -> List[Dict[str, Union[str, Union[int, float]]]] Lists the supported models. """ # jina models are not supported by this class - return [model for model in super().list_supported_models() if not model['model'].startswith('jinaai')] + return [model for model in super().list_supported_models() if not model["model"].startswith("jinaai")] class DefaultEmbedding(FlagEmbedding): @@ -548,11 +548,11 @@ class DefaultEmbedding(FlagEmbedding): """ def __init__( - self, - model_name: str = "BAAI/bge-small-en-v1.5", - max_length: int = 512, - cache_dir: Optional[str] = None, - threads: Optional[int] = None, + self, + model_name: str = "BAAI/bge-small-en-v1.5", + max_length: int = 512, + cache_dir: Optional[str] = None, + threads: Optional[int] = None, ): super().__init__(model_name, max_length=max_length, cache_dir=cache_dir, threads=threads) @@ -571,11 +571,11 @@ def embed(self, texts, batch_size: int = 256, parallel: int = None): class JinaEmbedding(Embedding): def __init__( - self, - model_name: str = "jinaai/jina-embeddings-v2-base-en", - max_length: int = 512, - cache_dir: str = None, - threads: int = None, + self, + model_name: str = "jinaai/jina-embeddings-v2-base-en", + max_length: int = 512, + cache_dir: str = None, + threads: int = None, ): """ Args: @@ -599,11 +599,10 @@ def __init__( self._model_dir = self.retrieve_model_hf(model_name, cache_dir) self._max_length = max_length - self.model = EmbeddingModel(self._model_dir, self.model_name, max_length=max_length, - max_threads=threads) + self.model = EmbeddingModel(self._model_dir, self.model_name, max_length=max_length, max_threads=threads) def embed( - self, documents: Union[str, Iterable[str]], batch_size: int = 256, parallel: int = None + self, documents: Union[str, Iterable[str]], batch_size: int = 256, parallel: int = None ) -> Iterable[np.ndarray]: """ Encode a list of documents into list of embeddings. @@ -653,7 +652,7 @@ def list_supported_models(cls) -> List[Dict[str, Union[str, Union[int, float]]]] Lists the supported models. """ # only jina models are supported by this class - return [model for model in Embedding.list_supported_models() if model['model'].startswith('jinaai')] + return [model for model in Embedding.list_supported_models() if model["model"].startswith("jinaai")] @staticmethod def mean_pooling(model_output, attention_mask): diff --git a/fastembed/parallel_processor.py b/fastembed/parallel_processor.py index 9df2eec9..9a0fb043 100644 --- a/fastembed/parallel_processor.py +++ b/fastembed/parallel_processor.py @@ -7,7 +7,7 @@ from multiprocessing.process import BaseProcess from multiprocessing.sharedctypes import Synchronized as BaseValue from queue import Empty -from typing import Any, Dict, Iterable, List, Optional, Type, Tuple +from typing import Any, Dict, Iterable, List, Optional, Tuple, Type # Single item should be processed in less than: processing_timeout = 10 * 60 # seconds diff --git a/fooling_around/Throughput_Across_Models.ipynb b/fooling_around/Throughput_Across_Models.ipynb index 279dfbea..6cfa31c5 100644 --- a/fooling_around/Throughput_Across_Models.ipynb +++ b/fooling_around/Throughput_Across_Models.ipynb @@ -20,14 +20,11 @@ "outputs": [], "source": [ "import time\n", - "from pathlib import Path\n", - "from typing import Any, Callable, List, Tuple\n", + "from typing import Callable, List, Tuple\n", "\n", - "import numpy as np\n", "import torch.nn.functional as F\n", - "from fastembed.embedding import DefaultEmbedding, Embedding\n", + "from fastembed.embedding import Embedding\n", "import matplotlib.pyplot as plt\n", - "from torch import Tensor\n", "from transformers import AutoModel, AutoTokenizer" ] }, @@ -126,6 +123,7 @@ " sentence_embeddings = F.normalize(sentence_embeddings)\n", " return sentence_embeddings\n", "\n", + "\n", "hf = HF(model_id=model_id)\n", "hf.embed(documents).shape" ] @@ -175,6 +173,8 @@ ], "source": [ "import types\n", + "\n", + "\n", "def calculate_time_stats(embed_func: Callable, documents: list, k: int) -> Tuple[float, float, float]:\n", " times = []\n", " for _ in range(k):\n", @@ -191,6 +191,7 @@ " # Returning mean, max, and min time for the call\n", " return (sum(times) / k, max(times), min(times))\n", "\n", + "\n", "hf_stats = calculate_time_stats(hf.embed, documents, k=2)\n", "print(f\"Huggingface Transformers (Average, Max, Min): {hf_stats}\")\n", "fst_stats = calculate_time_stats(embedding_model.embed, documents, k=2)\n", diff --git a/mkdocs.yml b/mkdocs.yml index fdc35997..ff995904 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -2,7 +2,7 @@ site_name: FastEmbed site_url: https://qdrant.github.io/fastembed/ site_author: Nirant Kasliwal repo_url: https://github.com/qdrant/fastembed/ -repo_name: qdrant/fastembed +repo_name: qdrant/fastembed remote_branch: gh-pages remote_name: origin @@ -33,11 +33,11 @@ theme: # Text color for primary color text: "#ffffff" - palette: + palette: # Palette toggle for light mode - scheme: default toggle: - icon: material/brightness-7 + icon: material/brightness-7 name: Switch to dark mode # Palette toggle for dark mode diff --git a/tests/profiling.py b/tests/profiling.py index 7b8349c4..9a07b551 100644 --- a/tests/profiling.py +++ b/tests/profiling.py @@ -1,27 +1,25 @@ # %% [markdown] # # 🤗 Huggingface vs ⚡ FastEmbed️ -# +# # Comparing the performance of Huggingface's 🤗 Transformers and ⚡ FastEmbed️ on a simple task on the following machine: Apple M2 Max, 32 GB RAM -# +# # ## 📦 Imports -# +# # Importing the necessary libraries for this comparison. # %% import time -from pathlib import Path -from typing import Any, Callable, List, Tuple +from typing import Callable, List, Tuple -import numpy as np +import matplotlib.pyplot as plt import torch.nn.functional as F -from torch import Tensor from transformers import AutoModel, AutoTokenizer from fastembed.embedding import DefaultEmbedding # %% [markdown] # ## 📖 Data -# +# # data is a list of strings, each string is a document. # %% @@ -43,9 +41,10 @@ # %% [markdown] # ## Setting up 🤗 Huggingface -# +# # We'll be using the [Huggingface Transformers](https://huggingface.co/transformers/) with PyTorch library to generate embeddings. We'll be using the same model across both libraries for a fair(er?) comparison. + # %% class HF: """ @@ -64,12 +63,13 @@ def embed(self, texts: List[str]): sentence_embeddings = F.normalize(sentence_embeddings) return sentence_embeddings + hf = HF(model_id="BAAI/bge-small-en") hf.embed(documents).shape # %% [markdown] # ## Setting up ⚡️FastEmbed -# +# # Sorry, don't have a lot to set up here. We'll be using the default model, which is Flag Embedding, same as the Huggingface model. # %% @@ -77,18 +77,19 @@ def embed(self, texts: List[str]): # %% [markdown] # ## 📊 Comparison -# +# # We'll be comparing the following metrics: Minimum, Maximum, Mean, across k runs. Let's write a function to do that: -# +# # ### 🚀 Calculating Stats + # %% def calculate_time_stats(embed_func: Callable, documents: list, k: int) -> Tuple[float, float, float]: times = [] for _ in range(k): # Timing the embed_func call start_time = time.time() - embeddings = embed_func(documents) + embed_func(documents) end_time = time.time() times.append(end_time - start_time) @@ -96,15 +97,13 @@ def calculate_time_stats(embed_func: Callable, documents: list, k: int) -> Tuple # Returning mean, max, and min time for the call return (sum(times) / k, max(times), min(times)) + # %% hf_stats = calculate_time_stats(hf.embed, documents, k=2) print(f"Huggingface Transformers (Average, Max, Min): {hf_stats}") fst_stats = calculate_time_stats(lambda x: list(embedding_model.embed(x)), documents, k=2) print(f"FastEmbed (Average, Max, Min): {fst_stats}") -# %% -import matplotlib.pyplot as plt - # %% def plot_character_per_second_comparison( @@ -141,5 +140,3 @@ def plot_character_per_second_comparison( plot_character_per_second_comparison(hf_stats, fst_stats, documents) - - diff --git a/tests/test_onnx_embeddings.py b/tests/test_onnx_embeddings.py index 45373ef0..c9af9e10 100644 --- a/tests/test_onnx_embeddings.py +++ b/tests/test_onnx_embeddings.py @@ -1,6 +1,7 @@ import os -import pytest + import numpy as np +import pytest from fastembed.embedding import DefaultEmbedding, JinaEmbedding @@ -17,7 +18,7 @@ } -@pytest.mark.parametrize('embedding_class', [DefaultEmbedding, JinaEmbedding]) +@pytest.mark.parametrize("embedding_class", [DefaultEmbedding, JinaEmbedding]) def test_embedding(embedding_class): is_ubuntu_ci = os.getenv("IS_UBUNTU_CI") @@ -34,10 +35,10 @@ def test_embedding(embedding_class): assert embeddings.shape == (2, dim) canonical_vector = CANONICAL_VECTOR_VALUES[model_desc["model"]] - assert np.allclose(embeddings[0, :canonical_vector.shape[0]], canonical_vector, atol=1e-3), model_desc["model"] + assert np.allclose(embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3), model_desc["model"] -@pytest.mark.parametrize('n_dims,embedding_class', [(384, DefaultEmbedding), (768, JinaEmbedding)]) +@pytest.mark.parametrize("n_dims,embedding_class", [(384, DefaultEmbedding), (768, JinaEmbedding)]) def test_batch_embedding(n_dims, embedding_class): model = embedding_class() @@ -48,7 +49,7 @@ def test_batch_embedding(n_dims, embedding_class): assert embeddings.shape == (200, n_dims) -@pytest.mark.parametrize('n_dims,embedding_class', [(384, DefaultEmbedding), (768, JinaEmbedding)]) +@pytest.mark.parametrize("n_dims,embedding_class", [(384, DefaultEmbedding), (768, JinaEmbedding)]) def test_parallel_processing(n_dims, embedding_class): model = embedding_class() From 5ac9a19cd30e85a918d9fde4fcdebfffd5ed6da8 Mon Sep 17 00:00:00 2001 From: Nirant Kasliwal Date: Wed, 17 Jan 2024 11:06:34 +0530 Subject: [PATCH 11/16] Add pre-commit package to pyproject.toml --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) 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echo "false") From da16752e57eb58e28970cbda722de33c1934e4f5 Mon Sep 17 00:00:00 2001 From: generall Date: Mon, 5 Feb 2024 15:12:32 +0100 Subject: [PATCH 16/16] rollback dependency changes --- .github/workflows/python-tests.yml | 2 +- poetry.lock | 1613 +--------------------------- pyproject.toml | 4 - 3 files changed, 12 insertions(+), 1607 deletions(-) diff --git a/.github/workflows/python-tests.yml b/.github/workflows/python-tests.yml index 11e117ca..171159c0 100644 --- a/.github/workflows/python-tests.yml +++ b/.github/workflows/python-tests.yml @@ -38,7 +38,7 @@ jobs: run: | python -m pip install poetry poetry config virtualenvs.create false - poetry install --no-interaction --no-ansi --without dev + poetry install --no-interaction --no-ansi - name: Run tests run: | export IS_UBUNTU_CI=$(test "${{ matrix.os }}" = "ubuntu-latest" && echo "true" || echo "false") diff --git a/poetry.lock b/poetry.lock index 0a6ba1f0..50b77c5d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,115 +1,5 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